Overview

Dataset statistics

Number of variables36
Number of observations873771
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory246.7 MiB
Average record size in memory296.0 B

Variable types

Numeric21
Categorical15

Alerts

device_fraud_count has constant value ""Constant
prev_address_months_count is highly overall correlated with current_address_months_countHigh correlation
current_address_months_count is highly overall correlated with prev_address_months_countHigh correlation
customer_age is highly overall correlated with date_of_birth_distinct_emails_4w and 1 other fieldsHigh correlation
velocity_24h is highly overall correlated with velocity_4w and 1 other fieldsHigh correlation
velocity_4w is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
bank_branch_count_8w is highly overall correlated with bank_months_countHigh correlation
credit_risk_score is highly overall correlated with proposed_credit_limitHigh correlation
bank_months_count is highly overall correlated with bank_branch_count_8wHigh correlation
proposed_credit_limit is highly overall correlated with credit_risk_scoreHigh correlation
month is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
date_of_birth_distinct_emails_4w is highly overall correlated with customer_ageHigh correlation
segmentacion_etaria is highly overall correlated with customer_ageHigh correlation
fraud_bool is highly imbalanced (91.3%)Imbalance
foreign_request is highly imbalanced (83.7%)Imbalance
source is highly imbalanced (93.5%)Imbalance
device_distinct_emails_8w is highly imbalanced (87.4%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
x1 has unique valuesUnique
x2 has unique valuesUnique
bank_branch_count_8w has 123789 (14.2%) zerosZeros
month has 70015 (8.0%) zerosZeros

Reproduction

Analysis started2023-05-17 22:17:30.592730
Analysis finished2023-05-17 22:22:44.822282
Duration5 minutes and 14.23 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct873771
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499986.08
Minimum0
Maximum999999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:44.952282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50029.5
Q1249948.5
median500153
Q3749761.5
95-th percentile949930.5
Maximum999999
Range999999
Interquartile range (IQR)499813

Descriptive statistics

Standard deviation288603.6
Coefficient of variation (CV)0.57722328
Kurtosis-1.1996867
Mean499986.08
Median Absolute Deviation (MAD)249909
Skewness-0.00032725316
Sum4.3687334 × 1011
Variance8.3292041 × 1010
MonotonicityNot monotonic
2023-05-17T18:22:45.105284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2047 1
 
< 0.1%
352760 1
 
< 0.1%
330229 1
 
< 0.1%
328180 1
 
< 0.1%
342515 1
 
< 0.1%
340466 1
 
< 0.1%
338417 1
 
< 0.1%
336368 1
 
< 0.1%
383471 1
 
< 0.1%
381422 1
 
< 0.1%
Other values (873761) 873761
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
999999 1
< 0.1%
999998 1
< 0.1%
999997 1
< 0.1%
999996 1
< 0.1%
999995 1
< 0.1%
999994 1
< 0.1%
999993 1
< 0.1%
999992 1
< 0.1%
999991 1
< 0.1%
999990 1
< 0.1%

fraud_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0.0
864149 
1.0
 
9622

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 864149
98.9%
1.0 9622
 
1.1%

Length

2023-05-17T18:22:45.223975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:45.337999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 864149
98.9%
1.0 9622
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1737920
66.3%
. 873771
33.3%
1 9622
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1737920
99.4%
1 9622
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1737920
66.3%
. 873771
33.3%
1 9622
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1737920
66.3%
. 873771
33.3%
1 9622
 
0.4%

income
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57872703
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:45.414581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.6
Q30.8
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28829172
Coefficient of variation (CV)0.49814801
Kurtosis-1.2207481
Mean0.57872703
Median Absolute Deviation (MAD)0.2
Skewness-0.46582304
Sum505674.9
Variance0.083112117
MonotonicityNot monotonic
2023-05-17T18:22:45.497091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 210628
24.1%
0.8 132325
15.1%
0.1 126834
14.5%
0.6 96055
11.0%
0.7 91894
10.5%
0.4 68662
 
7.9%
0.2 56720
 
6.5%
0.5 48014
 
5.5%
0.3 42639
 
4.9%
ValueCountFrequency (%)
0.1 126834
14.5%
0.2 56720
 
6.5%
0.3 42639
 
4.9%
0.4 68662
 
7.9%
0.5 48014
 
5.5%
0.6 96055
11.0%
0.7 91894
10.5%
0.8 132325
15.1%
0.9 210628
24.1%
ValueCountFrequency (%)
0.9 210628
24.1%
0.8 132325
15.1%
0.7 91894
10.5%
0.6 96055
11.0%
0.5 48014
 
5.5%
0.4 68662
 
7.9%
0.3 42639
 
4.9%
0.2 56720
 
6.5%
0.1 126834
14.5%

prev_address_months_count
Real number (ℝ)

Distinct373
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.664555
Minimum-1
Maximum399
Zeros0
Zeros (%)0.0%
Negative665984
Negative (%)76.2%
Memory size13.3 MiB
2023-05-17T18:22:45.620688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile96
Maximum399
Range400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation43.026325
Coefficient of variation (CV)2.9340355
Kurtosis22.371317
Mean14.664555
Median Absolute Deviation (MAD)0
Skewness4.321071
Sum12813463
Variance1851.2646
MonotonicityNot monotonic
2023-05-17T18:22:45.753818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 665984
76.2%
11 8131
 
0.9%
29 7252
 
0.8%
28 6974
 
0.8%
27 6887
 
0.8%
30 6791
 
0.8%
10 6551
 
0.7%
31 6261
 
0.7%
12 6219
 
0.7%
26 6044
 
0.7%
Other values (363) 146677
 
16.8%
ValueCountFrequency (%)
-1 665984
76.2%
6 25
 
< 0.1%
7 263
 
< 0.1%
8 1140
 
0.1%
9 3453
 
0.4%
10 6551
 
0.7%
11 8131
 
0.9%
12 6219
 
0.7%
13 3245
 
0.4%
14 1119
 
0.1%
ValueCountFrequency (%)
399 1
 
< 0.1%
386 1
 
< 0.1%
382 1
 
< 0.1%
376 1
 
< 0.1%
373 1
 
< 0.1%
372 1
 
< 0.1%
371 3
< 0.1%
370 4
< 0.1%
369 3
< 0.1%
368 6
< 0.1%
Distinct416
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.234073
Minimum-1
Maximum429
Zeros6777
Zeros (%)0.8%
Negative3005
Negative (%)0.3%
Memory size13.3 MiB
2023-05-17T18:22:45.892990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4
Q127
median64
Q3154
95-th percentile303
Maximum429
Range430
Interquartile range (IQR)127

Descriptive statistics

Standard deviation94.047441
Coefficient of variation (CV)0.94773336
Kurtosis0.6726646
Mean99.234073
Median Absolute Deviation (MAD)52
Skewness1.1723061
Sum86707855
Variance8844.9212
MonotonicityNot monotonic
2023-05-17T18:22:46.013767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 11978
 
1.4%
6 11869
 
1.4%
8 11857
 
1.4%
9 11616
 
1.3%
5 11241
 
1.3%
10 10996
 
1.3%
4 10708
 
1.2%
11 10408
 
1.2%
3 9960
 
1.1%
12 9563
 
1.1%
Other values (406) 763575
87.4%
ValueCountFrequency (%)
-1 3005
 
0.3%
0 6777
0.8%
1 7895
0.9%
2 9161
1.0%
3 9960
1.1%
4 10708
1.2%
5 11241
1.3%
6 11869
1.4%
7 11978
1.4%
8 11857
1.4%
ValueCountFrequency (%)
429 1
 
< 0.1%
423 1
 
< 0.1%
413 2
< 0.1%
412 2
< 0.1%
411 1
 
< 0.1%
410 2
< 0.1%
409 1
 
< 0.1%
408 3
< 0.1%
406 1
 
< 0.1%
405 4
< 0.1%

customer_age
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.344013
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:46.111653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median50
Q350
95-th percentile60
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.762575
Coefficient of variation (CV)0.33287952
Kurtosis-0.68064432
Mean41.344013
Median Absolute Deviation (MAD)10
Skewness-0.1963424
Sum36125200
Variance189.40848
MonotonicityNot monotonic
2023-05-17T18:22:46.366859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
50 334945
38.3%
30 164635
18.8%
20 128374
 
14.7%
40 127447
 
14.6%
60 87067
 
10.0%
70 16313
 
1.9%
10 11343
 
1.3%
80 3405
 
0.4%
90 242
 
< 0.1%
ValueCountFrequency (%)
10 11343
 
1.3%
20 128374
 
14.7%
30 164635
18.8%
40 127447
 
14.6%
50 334945
38.3%
60 87067
 
10.0%
70 16313
 
1.9%
80 3405
 
0.4%
90 242
 
< 0.1%
ValueCountFrequency (%)
90 242
 
< 0.1%
80 3405
 
0.4%
70 16313
 
1.9%
60 87067
 
10.0%
50 334945
38.3%
40 127447
 
14.6%
30 164635
18.8%
20 128374
 
14.7%
10 11343
 
1.3%

days_since_request
Real number (ℝ)

Distinct865279
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90266726
Minimum3.1127908 × 10-8
Maximum76.577505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:46.487855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.1127908 × 10-8
5-th percentile0.0014613274
Q10.0074489926
median0.015671089
Q30.026930162
95-th percentile3.64326
Maximum76.577505
Range76.577505
Interquartile range (IQR)0.01948117

Descriptive statistics

Standard deviation4.9968128
Coefficient of variation (CV)5.5356088
Kurtosis123.73928
Mean0.90266726
Median Absolute Deviation (MAD)0.0092771511
Skewness9.9755303
Sum788724.47
Variance24.968138
MonotonicityNot monotonic
2023-05-17T18:22:46.610771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.018708318 3
 
< 0.1%
0.02115635653 3
 
< 0.1%
0.02905488711 3
 
< 0.1%
0.03188848508 3
 
< 0.1%
0.01743084263 3
 
< 0.1%
0.01045490863 3
 
< 0.1%
0.02764367891 3
 
< 0.1%
0.02782556564 3
 
< 0.1%
0.01769853786 3
 
< 0.1%
0.01189230506 3
 
< 0.1%
Other values (865269) 873741
> 99.9%
ValueCountFrequency (%)
3.112790756 × 10-81
< 0.1%
4.284949667 × 10-81
< 0.1%
6.467590399 × 10-81
< 0.1%
6.668673265 × 10-81
< 0.1%
8.972571815 × 10-81
< 0.1%
1.184335368 × 10-71
< 0.1%
1.636929874 × 10-71
< 0.1%
1.936133843 × 10-71
< 0.1%
2.340225221 × 10-71
< 0.1%
2.459462076 × 10-71
< 0.1%
ValueCountFrequency (%)
76.57750471 1
< 0.1%
76.44178416 1
< 0.1%
76.35261379 1
< 0.1%
76.28622045 1
< 0.1%
76.10338234 1
< 0.1%
76.0644503 1
< 0.1%
76.03996532 1
< 0.1%
75.77016058 1
< 0.1%
75.54222839 1
< 0.1%
75.53508479 1
< 0.1%

intended_balcon_amount
Real number (ℝ)

Distinct869846
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5544251
Minimum-15.537329
Maximum112.7025
Zeros0
Zeros (%)0.0%
Negative656667
Negative (%)75.2%
Memory size13.3 MiB
2023-05-17T18:22:46.743762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-15.537329
5-th percentile-1.5820049
Q1-1.1797634
median-0.83456112
Q3-0.065957132
95-th percentile50.452651
Maximum112.7025
Range128.23983
Interquartile range (IQR)1.1138063

Descriptive statistics

Standard deviation20.528247
Coefficient of variation (CV)2.3997225
Kurtosis7.2088592
Mean8.5544251
Median Absolute Deviation (MAD)0.41419289
Skewness2.5851221
Sum7474608.5
Variance421.40891
MonotonicityNot monotonic
2023-05-17T18:22:46.865424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6157640927 3
 
< 0.1%
-1.209417833 3
 
< 0.1%
-0.8058495668 3
 
< 0.1%
-0.5522859925 3
 
< 0.1%
-0.6402193199 3
 
< 0.1%
-0.6140923978 3
 
< 0.1%
-1.335378318 3
 
< 0.1%
-0.8159297155 3
 
< 0.1%
-1.21798035 3
 
< 0.1%
-0.4931483641 3
 
< 0.1%
Other values (869836) 873741
> 99.9%
ValueCountFrequency (%)
-15.5373287 1
< 0.1%
-14.98174304 1
< 0.1%
-14.46186413 1
< 0.1%
-14.24921104 1
< 0.1%
-14.09788792 1
< 0.1%
-14.0649795 1
< 0.1%
-13.50054653 1
< 0.1%
-13.48327333 1
< 0.1%
-13.15491677 1
< 0.1%
-13.10520997 1
< 0.1%
ValueCountFrequency (%)
112.7025044 1
< 0.1%
112.613538 1
< 0.1%
112.4807053 1
< 0.1%
112.4618379 1
< 0.1%
112.3336455 1
< 0.1%
112.3298244 1
< 0.1%
112.1683185 1
< 0.1%
112.0994771 1
< 0.1%
112.0909752 1
< 0.1%
112.0881011 1
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
AB
348705 
AA
217944 
AC
215904 
AD
91011 
AE
 
207

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1747542
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAC
2nd rowAA
3rd rowAB
4th rowAB
5th rowAC

Common Values

ValueCountFrequency (%)
AB 348705
39.9%
AA 217944
24.9%
AC 215904
24.7%
AD 91011
 
10.4%
AE 207
 
< 0.1%

Length

2023-05-17T18:22:46.980524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:47.098953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ab 348705
39.9%
aa 217944
24.9%
ac 215904
24.7%
ad 91011
 
10.4%
ae 207
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 1091715
62.5%
B 348705
 
20.0%
C 215904
 
12.4%
D 91011
 
5.2%
E 207
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1747542
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1091715
62.5%
B 348705
 
20.0%
C 215904
 
12.4%
D 91011
 
5.2%
E 207
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1747542
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1091715
62.5%
B 348705
 
20.0%
C 215904
 
12.4%
D 91011
 
5.2%
E 207
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1747542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1091715
62.5%
B 348705
 
20.0%
C 215904
 
12.4%
D 91011
 
5.2%
E 207
 
< 0.1%

zip_count_4w
Real number (ℝ)

Distinct6222
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1517.7524
Minimum1
Maximum6650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:47.215034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile492
Q1886
median1208
Q31844
95-th percentile3544
Maximum6650
Range6649
Interquartile range (IQR)958

Descriptive statistics

Standard deviation965.19486
Coefficient of variation (CV)0.63593695
Kurtosis2.4504298
Mean1517.7524
Median Absolute Deviation (MAD)413
Skewness1.5415001
Sum1.3261681 × 109
Variance931601.11
MonotonicityNot monotonic
2023-05-17T18:22:47.336155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014 774
 
0.1%
924 771
 
0.1%
1023 770
 
0.1%
1021 768
 
0.1%
1048 764
 
0.1%
1062 761
 
0.1%
1054 759
 
0.1%
1033 753
 
0.1%
1065 752
 
0.1%
1093 750
 
0.1%
Other values (6212) 866149
99.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 5
< 0.1%
3 6
< 0.1%
4 2
 
< 0.1%
5 5
< 0.1%
6 4
< 0.1%
7 4
< 0.1%
8 6
< 0.1%
9 4
< 0.1%
10 9
< 0.1%
ValueCountFrequency (%)
6650 1
< 0.1%
6593 1
< 0.1%
6557 1
< 0.1%
6553 1
< 0.1%
6526 2
< 0.1%
6513 1
< 0.1%
6511 1
< 0.1%
6506 1
< 0.1%
6501 1
< 0.1%
6456 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct872758
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5490.9805
Minimum-174.10969
Maximum16754.959
Zeros0
Zeros (%)0.0%
Negative41
Negative (%)< 0.1%
Memory size13.3 MiB
2023-05-17T18:22:47.470362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-174.10969
5-th percentile1222.4558
Q13335.1508
median5190.9873
Q37369.1093
95-th percentile10877.642
Maximum16754.959
Range16929.069
Interquartile range (IQR)4033.9585

Descriptive statistics

Standard deviation2940.4648
Coefficient of variation (CV)0.53550815
Kurtosis0.14320413
Mean5490.9805
Median Absolute Deviation (MAD)1995.2764
Skewness0.60034026
Sum4.7978595 × 109
Variance8646333.2
MonotonicityNot monotonic
2023-05-17T18:22:47.590843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5869.79409 3
 
< 0.1%
5687.864752 2
 
< 0.1%
8779.64988 2
 
< 0.1%
4965.500762 2
 
< 0.1%
7850.923686 2
 
< 0.1%
2384.580864 2
 
< 0.1%
3608.889655 2
 
< 0.1%
5205.780625 2
 
< 0.1%
5393.118374 2
 
< 0.1%
5363.78358 2
 
< 0.1%
Other values (872748) 873750
> 99.9%
ValueCountFrequency (%)
-174.1096908 1
< 0.1%
-155.4307304 1
< 0.1%
-130.456928 1
< 0.1%
-113.0468992 1
< 0.1%
-110.7034762 1
< 0.1%
-106.9782971 1
< 0.1%
-96.51829979 1
< 0.1%
-84.13861148 1
< 0.1%
-77.95925234 1
< 0.1%
-75.12062215 1
< 0.1%
ValueCountFrequency (%)
16754.95902 1
< 0.1%
16754.20092 1
< 0.1%
16701.86995 1
< 0.1%
16573.28576 1
< 0.1%
16558.19839 1
< 0.1%
16528.15786 1
< 0.1%
16517.25703 1
< 0.1%
16515.61382 1
< 0.1%
16490.82418 1
< 0.1%
16472.27084 1
< 0.1%

velocity_24h
Real number (ℝ)

Distinct873005
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4660.4553
Minimum1322.3252
Maximum9539.3565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:47.722801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1322.3252
5-th percentile2550.9065
Q13502.7137
median4639.5885
Q35590.4609
95-th percentile7265.4883
Maximum9539.3565
Range8217.0314
Interquartile range (IQR)2087.7472

Descriptive statistics

Standard deviation1451.2839
Coefficient of variation (CV)0.31140388
Kurtosis-0.19749582
Mean4660.4553
Median Absolute Deviation (MAD)1050.579
Skewness0.42395826
Sum4.0721707 × 109
Variance2106224.9
MonotonicityNot monotonic
2023-05-17T18:22:47.843511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4197.278153 3
 
< 0.1%
5083.929288 3
 
< 0.1%
5594.539998 3
 
< 0.1%
4613.971468 2
 
< 0.1%
3046.498424 2
 
< 0.1%
4724.724845 2
 
< 0.1%
5574.216255 2
 
< 0.1%
5452.400235 2
 
< 0.1%
2768.896751 2
 
< 0.1%
4596.948245 2
 
< 0.1%
Other values (872995) 873748
> 99.9%
ValueCountFrequency (%)
1322.325176 1
< 0.1%
1326.681151 1
< 0.1%
1327.867307 1
< 0.1%
1330.702283 1
< 0.1%
1344.745469 1
< 0.1%
1346.622214 1
< 0.1%
1348.830318 1
< 0.1%
1366.255299 1
< 0.1%
1375.367356 1
< 0.1%
1375.998099 1
< 0.1%
ValueCountFrequency (%)
9539.35653 1
< 0.1%
9511.544062 1
< 0.1%
9505.599398 1
< 0.1%
9505.181514 1
< 0.1%
9501.092895 1
< 0.1%
9478.93622 1
< 0.1%
9474.913485 1
< 0.1%
9472.491584 1
< 0.1%
9468.552375 1
< 0.1%
9456.593159 1
< 0.1%

velocity_4w
Real number (ℝ)

Distinct872391
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4734.2333
Minimum2870.5916
Maximum7019.201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:47.971702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2870.5916
5-th percentile3115.4876
Q14238.4767
median4813.6843
Q35331.9199
95-th percentile6320.1441
Maximum7019.201
Range4148.6094
Interquartile range (IQR)1093.4431

Descriptive statistics

Standard deviation870.96841
Coefficient of variation (CV)0.18397243
Kurtosis-0.25252099
Mean4734.2333
Median Absolute Deviation (MAD)558.5276
Skewness-0.0093433282
Sum4.1366358 × 109
Variance758585.97
MonotonicityNot monotonic
2023-05-17T18:22:48.097746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4314.579619 3
 
< 0.1%
3480.307357 3
 
< 0.1%
4358.685619 3
 
< 0.1%
4237.957845 3
 
< 0.1%
4399.154479 2
 
< 0.1%
3906.341191 2
 
< 0.1%
4941.092661 2
 
< 0.1%
4425.5198 2
 
< 0.1%
3335.969981 2
 
< 0.1%
4780.087616 2
 
< 0.1%
Other values (872381) 873747
> 99.9%
ValueCountFrequency (%)
2870.591613 1
< 0.1%
2896.415063 1
< 0.1%
2918.632222 1
< 0.1%
2919.806878 1
< 0.1%
2920.415951 1
< 0.1%
2921.590392 1
< 0.1%
2922.163499 1
< 0.1%
2922.47631 1
< 0.1%
2926.560489 1
< 0.1%
2928.591401 1
< 0.1%
ValueCountFrequency (%)
7019.20103 1
< 0.1%
6977.711782 1
< 0.1%
6970.521948 1
< 0.1%
6961.908358 1
< 0.1%
6955.383287 1
< 0.1%
6942.150889 1
< 0.1%
6941.960129 1
< 0.1%
6940.29743 1
< 0.1%
6938.88494 1
< 0.1%
6938.768203 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2317
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.93924
Minimum0
Maximum2377
Zeros123789
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:48.223850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q331
95-th percentile1485
Maximum2377
Range2377
Interquartile range (IQR)30

Descriptive statistics

Standard deviation473.42035
Coefficient of variation (CV)2.3560374
Kurtosis5.5781624
Mean200.93924
Median Absolute Deviation (MAD)9
Skewness2.5742731
Sum1.7557488 × 108
Variance224126.83
MonotonicityNot monotonic
2023-05-17T18:22:48.352743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 130727
 
15.0%
0 123789
 
14.2%
2 51114
 
5.8%
11 26226
 
3.0%
10 25876
 
3.0%
12 25779
 
3.0%
13 24477
 
2.8%
9 24242
 
2.8%
14 22103
 
2.5%
8 22008
 
2.5%
Other values (2307) 397430
45.5%
ValueCountFrequency (%)
0 123789
14.2%
1 130727
15.0%
2 51114
 
5.8%
3 14145
 
1.6%
4 11488
 
1.3%
5 13852
 
1.6%
6 16681
 
1.9%
7 19513
 
2.2%
8 22008
 
2.5%
9 24242
 
2.8%
ValueCountFrequency (%)
2377 1
< 0.1%
2360 1
< 0.1%
2357 1
< 0.1%
2355 2
< 0.1%
2348 1
< 0.1%
2347 1
< 0.1%
2346 1
< 0.1%
2345 1
< 0.1%
2342 1
< 0.1%
2339 1
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
CA
598651 
CB
118080 
CC
77698 
CF
 
39257
CD
 
23311
Other values (2)
 
16774

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1747542
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC
2nd rowCF
3rd rowCC
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA 598651
68.5%
CB 118080
 
13.5%
CC 77698
 
8.9%
CF 39257
 
4.5%
CD 23311
 
2.7%
CE 16341
 
1.9%
CG 433
 
< 0.1%

Length

2023-05-17T18:22:48.463881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:48.578440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ca 598651
68.5%
cb 118080
 
13.5%
cc 77698
 
8.9%
cf 39257
 
4.5%
cd 23311
 
2.7%
ce 16341
 
1.9%
cg 433
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 951469
54.4%
A 598651
34.3%
B 118080
 
6.8%
F 39257
 
2.2%
D 23311
 
1.3%
E 16341
 
0.9%
G 433
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1747542
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 951469
54.4%
A 598651
34.3%
B 118080
 
6.8%
F 39257
 
2.2%
D 23311
 
1.3%
E 16341
 
0.9%
G 433
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1747542
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 951469
54.4%
A 598651
34.3%
B 118080
 
6.8%
F 39257
 
2.2%
D 23311
 
1.3%
E 16341
 
0.9%
G 433
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1747542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 951469
54.4%
A 598651
34.3%
B 118080
 
6.8%
F 39257
 
2.2%
D 23311
 
1.3%
E 16341
 
0.9%
G 433
 
< 0.1%

credit_risk_score
Real number (ℝ)

Distinct543
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.2571
Minimum-177
Maximum387
Zeros384
Zeros (%)< 0.1%
Negative10610
Negative (%)1.2%
Memory size13.3 MiB
2023-05-17T18:22:48.704187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-177
5-th percentile33
Q190
median130
Q3188
95-th percentile267
Maximum387
Range564
Interquartile range (IQR)98

Descriptive statistics

Standard deviation71.432183
Coefficient of variation (CV)0.51295182
Kurtosis-0.031287329
Mean139.2571
Median Absolute Deviation (MAD)48
Skewness0.28051542
Sum1.2167881 × 108
Variance5102.5567
MonotonicityNot monotonic
2023-05-17T18:22:48.825544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 5913
 
0.7%
108 5861
 
0.7%
113 5851
 
0.7%
116 5815
 
0.7%
115 5787
 
0.7%
112 5782
 
0.7%
107 5723
 
0.7%
106 5714
 
0.7%
109 5711
 
0.7%
105 5682
 
0.7%
Other values (533) 815932
93.4%
ValueCountFrequency (%)
-177 1
 
< 0.1%
-164 2
 
< 0.1%
-162 1
 
< 0.1%
-161 2
 
< 0.1%
-160 1
 
< 0.1%
-157 3
< 0.1%
-154 2
 
< 0.1%
-153 1
 
< 0.1%
-152 1
 
< 0.1%
-150 6
< 0.1%
ValueCountFrequency (%)
387 3
< 0.1%
386 3
< 0.1%
383 2
 
< 0.1%
382 1
 
< 0.1%
380 4
< 0.1%
378 2
 
< 0.1%
377 7
< 0.1%
376 6
< 0.1%
375 4
< 0.1%
374 7
< 0.1%

housing_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
BC
293000 
BB
263061 
BA
188394 
BE
104493 
BD
 
23146
Other values (2)
 
1677

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1747542
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBA
3rd rowBA
4th rowBE
5th rowBB

Common Values

ValueCountFrequency (%)
BC 293000
33.5%
BB 263061
30.1%
BA 188394
21.6%
BE 104493
 
12.0%
BD 23146
 
2.6%
BF 1443
 
0.2%
BG 234
 
< 0.1%

Length

2023-05-17T18:22:48.939062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:49.051029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
bc 293000
33.5%
bb 263061
30.1%
ba 188394
21.6%
be 104493
 
12.0%
bd 23146
 
2.6%
bf 1443
 
0.2%
bg 234
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 1136832
65.1%
C 293000
 
16.8%
A 188394
 
10.8%
E 104493
 
6.0%
D 23146
 
1.3%
F 1443
 
0.1%
G 234
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1747542
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1136832
65.1%
C 293000
 
16.8%
A 188394
 
10.8%
E 104493
 
6.0%
D 23146
 
1.3%
F 1443
 
0.1%
G 234
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1747542
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1136832
65.1%
C 293000
 
16.8%
A 188394
 
10.8%
E 104493
 
6.0%
D 23146
 
1.3%
F 1443
 
0.1%
G 234
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1747542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1136832
65.1%
C 293000
 
16.8%
A 188394
 
10.8%
E 104493
 
6.0%
D 23146
 
1.3%
F 1443
 
0.1%
G 234
 
< 0.1%

bank_months_count
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.140093
Minimum-1
Maximum32
Zeros0
Zeros (%)0.0%
Negative215991
Negative (%)24.7%
Memory size13.3 MiB
2023-05-17T18:22:49.164919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median6
Q325
95-th percentile30
Maximum32
Range33
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.12594
Coefficient of variation (CV)1.0884954
Kurtosis-1.4595453
Mean11.140093
Median Absolute Deviation (MAD)7
Skewness0.45256011
Sum9733890
Variance147.03841
MonotonicityNot monotonic
2023-05-17T18:22:49.389531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
-1 215991
24.7%
1 157374
18.0%
28 70915
 
8.1%
15 52506
 
6.0%
30 49165
 
5.6%
31 41364
 
4.7%
10 38504
 
4.4%
25 37025
 
4.2%
5 27891
 
3.2%
20 27096
 
3.1%
Other values (23) 155940
17.8%
ValueCountFrequency (%)
-1 215991
24.7%
1 157374
18.0%
2 23274
 
2.7%
3 7031
 
0.8%
4 3965
 
0.5%
5 27891
 
3.2%
6 16199
 
1.9%
7 695
 
0.1%
8 39
 
< 0.1%
9 5286
 
0.6%
ValueCountFrequency (%)
32 19
 
< 0.1%
31 41364
4.7%
30 49165
5.6%
29 8595
 
1.0%
28 70915
8.1%
27 3953
 
0.5%
26 21698
 
2.5%
25 37025
4.2%
24 1594
 
0.2%
23 230
 
< 0.1%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0.0
655869 
1.0
217902 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 655869
75.1%
1.0 217902
 
24.9%

Length

2023-05-17T18:22:49.488621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:49.583325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 655869
75.1%
1.0 217902
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0 1529640
58.4%
. 873771
33.3%
1 217902
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1529640
87.5%
1 217902
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1529640
58.4%
. 873771
33.3%
1 217902
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1529640
58.4%
. 873771
33.3%
1 217902
 
8.3%

proposed_credit_limit
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551.56971
Minimum190
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:49.674840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile200
Q1200
median200
Q31000
95-th percentile1500
Maximum2100
Range1910
Interquartile range (IQR)800

Descriptive statistics

Standard deviation506.76324
Coefficient of variation (CV)0.91876553
Kurtosis-0.23719709
Mean551.56971
Median Absolute Deviation (MAD)0
Skewness1.1431155
Sum4.8194562 × 108
Variance256808.98
MonotonicityNot monotonic
2023-05-17T18:22:49.760865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
200 506684
58.0%
1500 142808
 
16.3%
500 122477
 
14.0%
1000 79880
 
9.1%
2000 6995
 
0.8%
990 6732
 
0.8%
510 6344
 
0.7%
490 781
 
0.1%
210 507
 
0.1%
1900 404
 
< 0.1%
Other values (2) 159
 
< 0.1%
ValueCountFrequency (%)
190 106
 
< 0.1%
200 506684
58.0%
210 507
 
0.1%
490 781
 
0.1%
500 122477
 
14.0%
510 6344
 
0.7%
990 6732
 
0.8%
1000 79880
 
9.1%
1500 142808
 
16.3%
1900 404
 
< 0.1%
ValueCountFrequency (%)
2100 53
 
< 0.1%
2000 6995
 
0.8%
1900 404
 
< 0.1%
1500 142808
16.3%
1000 79880
9.1%
990 6732
 
0.8%
510 6344
 
0.7%
500 122477
14.0%
490 781
 
0.1%
210 507
 
0.1%

foreign_request
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0.0
852843 
1.0
 
20928

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 852843
97.6%
1.0 20928
 
2.4%

Length

2023-05-17T18:22:49.857380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:49.948581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 852843
97.6%
1.0 20928
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1726614
65.9%
. 873771
33.3%
1 20928
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1726614
98.8%
1 20928
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1726614
65.9%
. 873771
33.3%
1 20928
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1726614
65.9%
. 873771
33.3%
1 20928
 
0.8%

source
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
INTERNET
867069 
TELEAPP
 
6702

Length

Max length8
Median length8
Mean length7.9923298
Min length7

Characters and Unicode

Total characters6983466
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINTERNET
2nd rowINTERNET
3rd rowINTERNET
4th rowINTERNET
5th rowINTERNET

Common Values

ValueCountFrequency (%)
INTERNET 867069
99.2%
TELEAPP 6702
 
0.8%

Length

2023-05-17T18:22:50.031260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:50.125733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
internet 867069
99.2%
teleapp 6702
 
0.8%

Most occurring characters

ValueCountFrequency (%)
E 1747542
25.0%
T 1740840
24.9%
N 1734138
24.8%
R 867069
12.4%
I 867069
12.4%
P 13404
 
0.2%
A 6702
 
0.1%
L 6702
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6983466
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1747542
25.0%
T 1740840
24.9%
N 1734138
24.8%
R 867069
12.4%
I 867069
12.4%
P 13404
 
0.2%
A 6702
 
0.1%
L 6702
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6983466
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1747542
25.0%
T 1740840
24.9%
N 1734138
24.8%
R 867069
12.4%
I 867069
12.4%
P 13404
 
0.2%
A 6702
 
0.1%
L 6702
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6983466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1747542
25.0%
T 1740840
24.9%
N 1734138
24.8%
R 867069
12.4%
I 867069
12.4%
P 13404
 
0.2%
A 6702
 
0.1%
L 6702
 
0.1%

session_length_in_minutes
Real number (ℝ)

Distinct869648
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8145523
Minimum-1
Maximum85.567848
Zeros0
Zeros (%)0.0%
Negative1959
Negative (%)0.2%
Memory size13.3 MiB
2023-05-17T18:22:50.228720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.2661364
Q13.1504115
median5.2464566
Q39.3669249
95-th percentile22.532236
Maximum85.567848
Range86.567848
Interquartile range (IQR)6.2165133

Descriptive statistics

Standard deviation8.238858
Coefficient of variation (CV)1.0542969
Kurtosis13.689576
Mean7.8145523
Median Absolute Deviation (MAD)2.742281
Skewness3.1545882
Sum6828129.2
Variance67.878781
MonotonicityNot monotonic
2023-05-17T18:22:50.347752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1959
 
0.2%
4.706080005 3
 
< 0.1%
4.317986256 3
 
< 0.1%
5.771883817 3
 
< 0.1%
5.369191087 3
 
< 0.1%
4.766375488 3
 
< 0.1%
8.993731516 3
 
< 0.1%
4.455781343 3
 
< 0.1%
3.155039553 3
 
< 0.1%
2.603436545 2
 
< 0.1%
Other values (869638) 871786
99.8%
ValueCountFrequency (%)
-1 1959
0.2%
4.088611726 × 10-51
 
< 0.1%
0.001224497171 1
 
< 0.1%
0.00149964329 1
 
< 0.1%
0.001894059749 1
 
< 0.1%
0.003472779563 1
 
< 0.1%
0.004286417425 1
 
< 0.1%
0.006187519682 1
 
< 0.1%
0.007119304742 1
 
< 0.1%
0.007424412727 1
 
< 0.1%
ValueCountFrequency (%)
85.5678478 1
< 0.1%
85.16199762 1
< 0.1%
84.17860796 1
< 0.1%
83.37677458 1
< 0.1%
82.69866386 1
< 0.1%
82.34065183 1
< 0.1%
82.30868693 1
< 0.1%
81.95029589 1
< 0.1%
81.89104262 1
< 0.1%
81.83908038 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
linux
294611 
windows
265898 
other
262159 
macintosh
43852 
x11
 
7251

Length

Max length9
Median length5
Mean length5.7927729
Min length3

Characters and Unicode

Total characters5061557
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwindows
2nd rowlinux
3rd rowlinux
4th rowwindows
5th rowother

Common Values

ValueCountFrequency (%)
linux 294611
33.7%
windows 265898
30.4%
other 262159
30.0%
macintosh 43852
 
5.0%
x11 7251
 
0.8%

Length

2023-05-17T18:22:50.459795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:50.569029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
linux 294611
33.7%
windows 265898
30.4%
other 262159
30.0%
macintosh 43852
 
5.0%
x11 7251
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i 604361
11.9%
n 604361
11.9%
o 571909
11.3%
w 531796
10.5%
s 309750
 
6.1%
h 306011
 
6.0%
t 306011
 
6.0%
x 301862
 
6.0%
u 294611
 
5.8%
l 294611
 
5.8%
Other values (7) 936274
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5047055
99.7%
Decimal Number 14502
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 604361
12.0%
n 604361
12.0%
o 571909
11.3%
w 531796
10.5%
s 309750
 
6.1%
h 306011
 
6.1%
t 306011
 
6.1%
x 301862
 
6.0%
u 294611
 
5.8%
l 294611
 
5.8%
Other values (6) 921772
18.3%
Decimal Number
ValueCountFrequency (%)
1 14502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5047055
99.7%
Common 14502
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 604361
12.0%
n 604361
12.0%
o 571909
11.3%
w 531796
10.5%
s 309750
 
6.1%
h 306011
 
6.1%
t 306011
 
6.1%
x 301862
 
6.0%
u 294611
 
5.8%
l 294611
 
5.8%
Other values (6) 921772
18.3%
Common
ValueCountFrequency (%)
1 14502
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5061557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 604361
11.9%
n 604361
11.9%
o 571909
11.3%
w 531796
10.5%
s 309750
 
6.1%
h 306011
 
6.0%
t 306011
 
6.0%
x 301862
 
6.0%
u 294611
 
5.8%
l 294611
 
5.8%
Other values (7) 936274
18.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1.0
485669 
0.0
388102 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 485669
55.6%
0.0 388102
44.4%

Length

2023-05-17T18:22:50.669594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:50.763644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 485669
55.6%
0.0 388102
44.4%

Most occurring characters

ValueCountFrequency (%)
0 1261873
48.1%
. 873771
33.3%
1 485669
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1261873
72.2%
1 485669
 
27.8%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1261873
48.1%
. 873771
33.3%
1 485669
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1261873
48.1%
. 873771
33.3%
1 485669
 
18.5%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0.0
873771 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 873771
100.0%

Length

2023-05-17T18:22:50.846733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:50.938442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 873771
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1747542
66.7%
. 873771
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1747542
100.0%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1747542
66.7%
. 873771
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1747542
66.7%
. 873771
33.3%

month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6576838
Minimum0
Maximum7
Zeros70015
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:51.010978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1170502
Coefficient of variation (CV)0.57879531
Kurtosis-1.0857408
Mean3.6576838
Median Absolute Deviation (MAD)2
Skewness-0.057323507
Sum3195978
Variance4.4819016
MonotonicityNot monotonic
2023-05-17T18:22:51.096081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 136716
15.6%
5 128765
14.7%
2 124587
14.3%
6 118909
13.6%
4 110464
12.6%
7 92201
10.6%
1 92114
10.5%
0 70015
8.0%
ValueCountFrequency (%)
0 70015
8.0%
1 92114
10.5%
2 124587
14.3%
3 136716
15.6%
4 110464
12.6%
5 128765
14.7%
6 118909
13.6%
7 92201
10.6%
ValueCountFrequency (%)
7 92201
10.6%
6 118909
13.6%
5 128765
14.7%
4 110464
12.6%
3 136716
15.6%
2 124587
14.3%
1 92114
10.5%
0 70015
8.0%

x1
Real number (ℝ)

Distinct873771
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014021731
Minimum-4.9778644
Maximum6.4348669
Zeros0
Zeros (%)0.0%
Negative434566
Negative (%)49.7%
Memory size13.3 MiB
2023-05-17T18:22:51.212744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.9778644
5-th percentile-1.6364132
Q1-0.66833462
median0.0067719363
Q30.68669255
95-th percentile1.683451
Maximum6.4348669
Range11.412731
Interquartile range (IQR)1.3550272

Descriptive statistics

Standard deviation1.0133589
Coefficient of variation (CV)72.270599
Kurtosis0.14866166
Mean0.014021731
Median Absolute Deviation (MAD)0.67750554
Skewness0.065740992
Sum12251.782
Variance1.0268962
MonotonicityNot monotonic
2023-05-17T18:22:51.337968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.595567671 1
 
< 0.1%
1.045397697 1
 
< 0.1%
-0.003644725236 1
 
< 0.1%
1.071254918 1
 
< 0.1%
0.6512748066 1
 
< 0.1%
0.6155939734 1
 
< 0.1%
0.1875247825 1
 
< 0.1%
0.1033051692 1
 
< 0.1%
0.2523483051 1
 
< 0.1%
0.3152529289 1
 
< 0.1%
Other values (873761) 873761
> 99.9%
ValueCountFrequency (%)
-4.977864446 1
< 0.1%
-4.913331616 1
< 0.1%
-4.852117653 1
< 0.1%
-4.659952967 1
< 0.1%
-4.602974619 1
< 0.1%
-4.446632241 1
< 0.1%
-4.371314395 1
< 0.1%
-4.365340579 1
< 0.1%
-4.233164797 1
< 0.1%
-4.215015696 1
< 0.1%
ValueCountFrequency (%)
6.434866931 1
< 0.1%
5.938492819 1
< 0.1%
5.647253063 1
< 0.1%
5.605919708 1
< 0.1%
5.518075554 1
< 0.1%
5.497664426 1
< 0.1%
5.481411648 1
< 0.1%
5.35703142 1
< 0.1%
5.335605496 1
< 0.1%
5.187241498 1
< 0.1%

x2
Real number (ℝ)

Distinct873771
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011190994
Minimum-4.8464137
Maximum6.5424922
Zeros0
Zeros (%)0.0%
Negative434928
Negative (%)49.8%
Memory size13.3 MiB
2023-05-17T18:22:51.461640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.8464137
5-th percentile-1.6390596
Q1-0.67030641
median0.0054820987
Q30.68343093
95-th percentile1.680323
Maximum6.5424922
Range11.388906
Interquartile range (IQR)1.3537373

Descriptive statistics

Standard deviation1.0124485
Coefficient of variation (CV)90.469936
Kurtosis0.14941181
Mean0.011190994
Median Absolute Deviation (MAD)0.676845
Skewness0.066481802
Sum9778.3657
Variance1.0250519
MonotonicityNot monotonic
2023-05-17T18:22:51.584866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4132562051 1
 
< 0.1%
-0.9117121251 1
 
< 0.1%
-1.451227144 1
 
< 0.1%
-0.181849659 1
 
< 0.1%
2.011186416 1
 
< 0.1%
-1.008846319 1
 
< 0.1%
-0.1771089208 1
 
< 0.1%
-0.7423529176 1
 
< 0.1%
1.531585647 1
 
< 0.1%
0.2147573061 1
 
< 0.1%
Other values (873761) 873761
> 99.9%
ValueCountFrequency (%)
-4.846413733 1
< 0.1%
-4.783369547 1
< 0.1%
-4.618998589 1
< 0.1%
-4.433939506 1
< 0.1%
-4.426691766 1
< 0.1%
-4.3132024 1
< 0.1%
-4.302575372 1
< 0.1%
-4.296208593 1
< 0.1%
-4.28748851 1
< 0.1%
-4.257022333 1
< 0.1%
ValueCountFrequency (%)
6.542492182 1
< 0.1%
6.284777011 1
< 0.1%
6.028632545 1
< 0.1%
5.951461793 1
< 0.1%
5.640478546 1
< 0.1%
5.487140277 1
< 0.1%
5.460212461 1
< 0.1%
5.33297376 1
< 0.1%
5.307578982 1
< 0.1%
5.217863628 1
< 0.1%

name_email_similarity
Real number (ℝ)

Distinct872778
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48771499
Minimum5.0247067 × 10-8
Maximum0.99999996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:51.715571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.0247067 × 10-8
5-th percentile0.067003863
Q10.21468263
median0.48603337
Q30.75462222
95-th percentile0.91665107
Maximum0.99999996
Range0.99999991
Interquartile range (IQR)0.53993959

Descriptive statistics

Standard deviation0.29142273
Coefficient of variation (CV)0.59752671
Kurtosis-1.3088828
Mean0.48771499
Median Absolute Deviation (MAD)0.27010353
Skewness0.070488446
Sum426151.21
Variance0.084927208
MonotonicityNot monotonic
2023-05-17T18:22:51.840961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7941685058 2
 
< 0.1%
0.1628446049 2
 
< 0.1%
0.8845038975 2
 
< 0.1%
0.7087152073 2
 
< 0.1%
0.7096672396 2
 
< 0.1%
0.288396699 2
 
< 0.1%
0.1992724747 2
 
< 0.1%
0.04065908753 2
 
< 0.1%
0.2902594471 2
 
< 0.1%
0.1821337959 2
 
< 0.1%
Other values (872768) 873751
> 99.9%
ValueCountFrequency (%)
5.024706719 × 10-81
< 0.1%
7.898993833 × 10-71
< 0.1%
6.11675322 × 10-61
< 0.1%
8.85821685 × 10-61
< 0.1%
1.887405922 × 10-51
< 0.1%
2.022443363 × 10-51
< 0.1%
2.116087418 × 10-51
< 0.1%
2.539521405 × 10-51
< 0.1%
3.198422688 × 10-51
< 0.1%
3.548570023 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999999633 1
< 0.1%
0.9999998641 1
< 0.1%
0.9999996731 1
< 0.1%
0.9999990265 1
< 0.1%
0.9999984639 1
< 0.1%
0.9999982377 1
< 0.1%
0.9999981359 1
< 0.1%
0.9999977445 1
< 0.1%
0.9999976263 1
< 0.1%
0.9999974194 1
< 0.1%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7732907
Minimum0
Maximum39
Zeros3174
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size13.3 MiB
2023-05-17T18:22:51.983577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q311
95-th percentile17
Maximum39
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8153014
Coefficient of variation (CV)0.61946756
Kurtosis1.0324455
Mean7.7732907
Median Absolute Deviation (MAD)3
Skewness0.99832431
Sum6792076
Variance23.187128
MonotonicityNot monotonic
2023-05-17T18:22:52.113329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 94762
10.8%
6 84497
 
9.7%
4 80098
 
9.2%
7 75965
 
8.7%
8 64667
 
7.4%
3 62971
 
7.2%
2 62764
 
7.2%
9 48279
 
5.5%
11 47740
 
5.5%
10 40536
 
4.6%
Other values (30) 211492
24.2%
ValueCountFrequency (%)
0 3174
 
0.4%
1 30406
 
3.5%
2 62764
7.2%
3 62971
7.2%
4 80098
9.2%
5 94762
10.8%
6 84497
9.7%
7 75965
8.7%
8 64667
7.4%
9 48279
5.5%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 4
 
< 0.1%
37 8
 
< 0.1%
36 12
 
< 0.1%
35 40
 
< 0.1%
34 49
 
< 0.1%
33 63
 
< 0.1%
32 101
< 0.1%
31 134
< 0.1%
30 196
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1.0
453162 
0.0
420609 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 453162
51.9%
0.0 420609
48.1%

Length

2023-05-17T18:22:52.225790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:52.327233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 453162
51.9%
0.0 420609
48.1%

Most occurring characters

ValueCountFrequency (%)
0 1294380
49.4%
. 873771
33.3%
1 453162
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1294380
74.1%
1 453162
 
25.9%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1294380
49.4%
. 873771
33.3%
1 453162
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1294380
49.4%
. 873771
33.3%
1 453162
 
17.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1.0
842074 
2.0
 
25681
0.0
 
5735
-1.0
 
281

Length

Max length4
Median length3
Mean length3.0003216
Min length3

Characters and Unicode

Total characters2621594
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 842074
96.4%
2.0 25681
 
2.9%
0.0 5735
 
0.7%
-1.0 281
 
< 0.1%

Length

2023-05-17T18:22:52.622967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:52.778993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 842355
96.4%
2.0 25681
 
2.9%
0.0 5735
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 879506
33.5%
. 873771
33.3%
1 842355
32.1%
2 25681
 
1.0%
- 281
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%
Dash Punctuation 281
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 879506
50.3%
1 842355
48.2%
2 25681
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 281
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621594
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 879506
33.5%
. 873771
33.3%
1 842355
32.1%
2 25681
 
1.0%
- 281
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 879506
33.5%
. 873771
33.3%
1 842355
32.1%
2 25681
 
1.0%
- 281
 
< 0.1%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0.0
442588 
1.0
431183 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 442588
50.7%
1.0 431183
49.3%

Length

2023-05-17T18:22:52.874757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:52.996601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 442588
50.7%
1.0 431183
49.3%

Most occurring characters

ValueCountFrequency (%)
0 1316359
50.2%
. 873771
33.3%
1 431183
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1316359
75.3%
1 431183
 
24.7%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1316359
50.2%
. 873771
33.3%
1 431183
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1316359
50.2%
. 873771
33.3%
1 431183
 
16.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1.0
748761 
0.0
125010 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 748761
85.7%
0.0 125010
 
14.3%

Length

2023-05-17T18:22:53.086766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:53.189631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 748761
85.7%
0.0 125010
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 998781
38.1%
. 873771
33.3%
1 748761
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 998781
57.2%
1 748761
42.8%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 998781
38.1%
. 873771
33.3%
1 748761
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 998781
38.1%
. 873771
33.3%
1 748761
28.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
Adulto
627027 
Adulto-Joven
128374 
Persona Mayor
107027 
Joven
 
11343

Length

Max length13
Median length6
Mean length7.7259557
Min length5

Characters and Unicode

Total characters6750716
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersona Mayor
2nd rowAdulto
3rd rowAdulto
4th rowAdulto
5th rowAdulto

Common Values

ValueCountFrequency (%)
Adulto 627027
71.8%
Adulto-Joven 128374
 
14.7%
Persona Mayor 107027
 
12.2%
Joven 11343
 
1.3%

Length

2023-05-17T18:22:53.294609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T18:22:53.420601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
adulto 627027
63.9%
adulto-joven 128374
 
13.1%
mayor 107027
 
10.9%
persona 107027
 
10.9%
joven 11343
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 1109172
16.4%
A 755401
11.2%
d 755401
11.2%
u 755401
11.2%
l 755401
11.2%
t 755401
11.2%
n 246744
 
3.7%
e 246744
 
3.7%
a 214054
 
3.2%
r 214054
 
3.2%
Other values (8) 942943
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5406143
80.1%
Uppercase Letter 1109172
 
16.4%
Dash Punctuation 128374
 
1.9%
Space Separator 107027
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1109172
20.5%
d 755401
14.0%
u 755401
14.0%
l 755401
14.0%
t 755401
14.0%
n 246744
 
4.6%
e 246744
 
4.6%
a 214054
 
4.0%
r 214054
 
4.0%
v 139717
 
2.6%
Other values (2) 214054
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 755401
68.1%
J 139717
 
12.6%
M 107027
 
9.6%
P 107027
 
9.6%
Dash Punctuation
ValueCountFrequency (%)
- 128374
100.0%
Space Separator
ValueCountFrequency (%)
107027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6515315
96.5%
Common 235401
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1109172
17.0%
A 755401
11.6%
d 755401
11.6%
u 755401
11.6%
l 755401
11.6%
t 755401
11.6%
n 246744
 
3.8%
e 246744
 
3.8%
a 214054
 
3.3%
r 214054
 
3.3%
Other values (6) 707542
10.9%
Common
ValueCountFrequency (%)
- 128374
54.5%
107027
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6750716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1109172
16.4%
A 755401
11.2%
d 755401
11.2%
u 755401
11.2%
l 755401
11.2%
t 755401
11.2%
n 246744
 
3.7%
e 246744
 
3.7%
a 214054
 
3.2%
r 214054
 
3.2%
Other values (8) 942943
14.0%

Interactions

2023-05-17T18:22:28.753131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:42.404178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:50.711200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:58.399689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:05.959862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:14.192904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:22.322860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:30.325715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:38.340260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:47.202011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:56.180737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:04.627780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:14.018485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:22.235830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:30.424082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:38.914501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:47.046770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:55.363755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:03.737660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:11.946092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:20.266678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:29.172761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:42.809841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:51.057842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:58.767923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:06.337641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:14.631569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:22.719693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:30.692657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:38.733109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:47.607697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:56.583498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:05.016846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:14.412135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:22.625563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:30.801805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:39.302968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:47.420901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:55.760983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:04.147839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:12.325922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:20.799891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:29.703777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:43.197838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:51.416835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:59.144129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:06.852808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:15.024546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:23.156717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:31.064728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:39.165655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:48.016513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:56.978562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:05.422822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:14.897608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-05-17T18:20:10.654665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:18.828817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:27.010933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:34.900761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:43.225554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:52.319864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:00.899419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:10.168316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:18.723769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:26.902782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:35.225662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:43.533752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:51.839693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:59.997673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:08.422639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:16.566400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:25.200054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:34.514967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:47.648319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:55.500947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:03.057872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:11.037203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:19.206123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:27.380402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:35.274939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:43.631573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:52.799208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:01.302062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:10.608965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:19.104184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:27.293445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:35.643263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:43.919819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:52.228966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:00.381591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:08.813735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:17.095908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:25.605865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:34.952018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:48.035996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:55.873643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:03.399879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:11.439690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:19.568078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:27.747642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:35.650084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:44.079766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:53.219797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:01.678319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:11.086785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:19.471017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:27.673005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:36.073784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:44.306646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:52.603773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:00.781746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:09.224649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:17.476417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:26.013331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:35.370662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:48.429823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:56.248151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:03.756083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:11.843503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:19.934812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:28.128529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:36.035663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:44.517208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:53.645756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:02.113842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:11.539705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:19.980082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:28.073934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:36.500654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:44.698037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:52.980611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:01.181594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:09.618855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:17.860539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:26.399382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:35.761881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:48.804849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:56.591583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:04.148899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:12.233266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:20.300864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:28.482364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:36.395715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:44.932734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:54.040629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:02.520036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:11.932432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:20.348681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:28.457929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:36.905562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:45.086629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:53.355610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:01.593697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:10.006903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:18.253994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:26.779287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:36.190670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:49.191952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:56.941835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:04.497439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:12.613854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:20.807837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:28.837919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:36.767664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:45.381612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:54.448987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:02.919774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:12.337524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:20.733687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:28.842791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:37.322823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:45.474971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:53.739672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:02.035199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:10.374756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:18.641575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:27.179007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:36.585011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:49.563982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:57.302809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:04.837914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:12.990807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:21.175960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:29.207777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:37.131953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:45.910640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:54.868694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:03.307597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:12.759722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:21.108801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:29.238780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:37.714596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:45.846498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:54.143753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:02.562097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:10.766537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:19.018594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:27.577798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:37.017774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:49.937080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:57.643846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:05.213976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:13.367878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:21.540167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:29.575802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:37.510256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:46.365855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:55.315738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:03.831652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:13.189237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:21.473829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:29.619470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:38.114988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:46.233643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:54.550523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:02.929209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:11.168022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:19.391724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:27.955923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:37.429697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:50.326033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:19:57.992816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:05.563085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:13.774588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:21.930034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:29.936794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:37.891740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:46.787574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:20:55.751300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:04.224895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:13.607524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:21.838793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:30.032338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:38.509689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:46.642977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:21:54.962796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:03.339674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:11.552492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:19.810855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-17T18:22:28.349413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-05-17T18:22:53.566579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
idincomeprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountzip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wcredit_risk_scorebank_months_countproposed_credit_limitsession_length_in_minutesmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wfraud_boolpayment_typeemployment_statushousing_statushas_other_cardsforeign_requestsourcedevice_oskeep_alive_sessionemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_validsegmentacion_etaria
id1.0000.001-0.0010.0000.001-0.0000.0000.000-0.000-0.001-0.000-0.0000.0010.0010.001-0.0010.001-0.0010.0010.000-0.0010.0020.0000.0020.0010.0020.0000.0000.0000.0000.0000.0010.0040.0020.000
income0.0011.0000.008-0.0190.087-0.0280.108-0.079-0.100-0.114-0.1220.0140.1860.0070.142-0.0840.1310.0040.002-0.033-0.0380.0520.0310.0610.0850.0880.0130.0140.0400.0390.0250.0160.0130.0370.076
prev_address_months_count-0.0010.0081.000-0.611-0.1640.016-0.033-0.0690.0060.0250.023-0.070-0.055-0.076-0.0580.070-0.014-0.003-0.002-0.0320.1530.0240.0420.0250.0510.0450.0160.0120.0240.0400.0220.0110.0510.0230.035
current_address_months_count0.000-0.019-0.6111.0000.225-0.0200.0930.0560.0180.0050.0140.0450.1320.0750.156-0.040-0.0130.0010.0010.046-0.2310.0500.0570.0630.1800.0760.0220.0160.0530.0640.0890.0180.1420.1110.106
customer_age0.0010.087-0.1640.2251.0000.039-0.014-0.0240.0020.008-0.0060.0600.1400.0470.1280.0540.013-0.006-0.005-0.044-0.5600.0330.0490.1660.1800.1170.0110.0320.0930.0670.0410.0370.2530.1741.000
days_since_request-0.000-0.0280.016-0.0200.0391.000-0.044-0.0340.0710.0540.0310.019-0.101-0.002-0.0850.059-0.031-0.001-0.002-0.033-0.0570.0060.0730.0140.0290.0550.0020.0180.0150.0020.0180.0120.0530.0150.020
intended_balcon_amount0.0000.108-0.0330.093-0.014-0.0441.0000.0070.0350.0650.0670.1600.0290.1960.1000.045-0.051-0.000-0.0030.061-0.0050.0310.4350.0380.0760.1330.0120.0150.0570.0290.0210.0250.0260.0630.020
zip_count_4w0.000-0.079-0.0690.056-0.024-0.0340.0071.0000.1280.1980.2690.012-0.0920.048-0.0240.047-0.268-0.0010.0030.0180.0980.0140.0540.0370.0360.0560.0240.0160.0200.0390.0270.0190.0880.0360.051
velocity_6h-0.000-0.1000.0060.0180.0020.0710.0350.1281.0000.4570.3840.018-0.1430.014-0.0580.064-0.396-0.0000.0010.0230.0680.0160.0680.0370.0420.0430.0100.0160.0390.0350.0450.0270.0530.0310.034
velocity_24h-0.001-0.1140.0250.0050.0080.0540.0650.1980.4571.0000.5280.037-0.1440.017-0.0190.087-0.539-0.0010.0000.0260.0950.0120.0610.0400.0380.0590.0200.0190.0270.0510.0480.0370.0560.0410.031
velocity_4w-0.000-0.1220.0230.014-0.0060.0310.0670.2690.3840.5281.0000.034-0.1730.0190.0040.112-0.837-0.001-0.0000.0540.1520.0240.0730.0440.0610.0830.0330.0260.0510.0990.0520.0520.0930.0700.027
bank_branch_count_8w-0.0000.014-0.0700.0450.0600.0190.1600.0120.0180.0370.0341.000-0.0320.5010.0060.009-0.038-0.004-0.002-0.023-0.0290.0200.1430.0210.0280.0640.0070.0190.0270.0120.0110.0120.0750.0240.042
credit_risk_score0.0010.186-0.0550.1320.140-0.1010.029-0.092-0.143-0.144-0.173-0.0321.000-0.0440.661-0.0420.1670.0040.0040.060-0.1140.0790.0470.0640.1430.1390.0150.0210.0710.0410.0340.0380.0190.0340.083
bank_months_count0.0010.007-0.0760.0750.047-0.0020.1960.0480.0140.0170.0190.501-0.0441.000-0.0060.022-0.016-0.004-0.000-0.018-0.0340.0270.2930.0570.0430.0470.0120.0360.0360.0310.0190.0230.0810.0310.052
proposed_credit_limit0.0010.142-0.0580.1560.128-0.0850.100-0.024-0.058-0.0190.0040.0060.661-0.0061.000-0.004-0.0110.0040.0030.082-0.0540.1000.0580.0630.1450.1290.0280.0170.0650.0610.0460.0280.0470.0300.077
session_length_in_minutes-0.001-0.0840.070-0.0400.0540.0590.0450.0470.0640.0870.1120.009-0.0420.022-0.0041.000-0.1040.000-0.0010.022-0.0530.0160.0290.0330.0290.1210.0100.0420.0340.0490.0440.0500.0380.0170.025
month0.0010.131-0.014-0.0130.013-0.031-0.051-0.268-0.396-0.539-0.837-0.0380.167-0.016-0.011-0.1041.000-0.000-0.000-0.042-0.1630.0260.0760.0450.0660.0880.0290.0260.0540.1110.0660.0520.1010.0750.030
x1-0.0010.004-0.0030.001-0.006-0.001-0.000-0.001-0.000-0.001-0.001-0.0040.004-0.0040.0040.000-0.0001.0000.015-0.0050.0010.2810.0070.0040.0090.0120.0020.0000.0080.0130.0090.0040.0180.0020.007
x20.0010.002-0.0020.001-0.005-0.002-0.0030.0030.0010.000-0.000-0.0020.004-0.0000.003-0.001-0.0000.0151.000-0.0020.0020.2840.0060.0050.0100.0120.0050.0000.0090.0120.0090.0050.0190.0030.007
name_email_similarity0.000-0.033-0.0320.046-0.044-0.0330.0610.0180.0230.0260.054-0.0230.060-0.0180.0820.022-0.042-0.005-0.0021.0000.0420.0390.0690.0420.0450.0380.0250.0120.0430.0440.0740.0200.0300.0430.046
date_of_birth_distinct_emails_4w-0.001-0.0380.153-0.231-0.560-0.057-0.0050.0980.0680.0950.152-0.029-0.114-0.034-0.054-0.053-0.1630.0010.0020.0421.0000.0300.0770.1520.0970.0580.0300.0290.0550.0600.0530.0380.2220.1420.255
fraud_bool0.0020.0520.0240.0500.0330.0060.0310.0140.0160.0120.0240.0200.0790.0270.1000.0160.0260.2810.2840.0390.0301.0000.0410.0330.0980.0380.0170.0030.0730.0480.0280.0400.0460.0080.025
payment_type0.0000.0310.0420.0570.0490.0730.4350.0540.0680.0610.0730.1430.0470.2930.0580.0290.0760.0070.0060.0690.0770.0411.0000.0590.1020.1570.0270.0640.0630.0310.0300.0450.0840.0690.042
employment_status0.0020.0610.0250.0630.1660.0140.0380.0370.0370.0400.0440.0210.0640.0570.0630.0330.0450.0040.0050.0420.1520.0330.0591.0000.1150.0390.0240.0370.0630.0700.0160.0430.1740.1770.216
housing_status0.0010.0850.0510.1800.1800.0290.0760.0360.0420.0380.0610.0280.1430.0430.1450.0290.0660.0090.0100.0450.0970.0980.1020.1151.0000.0730.0280.0230.0790.0660.0880.0250.1170.0980.235
has_other_cards0.0020.0880.0450.0760.1170.0550.1330.0560.0430.0590.0830.0640.1390.0470.1290.1210.0880.0120.0120.0380.0580.0380.1570.0390.0731.0000.0040.0140.0440.0860.0330.0340.1050.0170.110
foreign_request0.0000.0130.0160.0220.0110.0020.0120.0240.0100.0200.0330.0070.0150.0120.0280.0100.0290.0020.0050.0250.0300.0170.0270.0240.0280.0041.0000.0070.0530.0150.0290.0070.0090.0090.007
source0.0000.0140.0120.0160.0320.0180.0150.0160.0160.0190.0260.0190.0210.0360.0170.0420.0260.0000.0000.0120.0290.0030.0640.0370.0230.0140.0071.0000.0840.0860.0010.4210.0110.0240.028
device_os0.0000.0400.0240.0530.0930.0150.0570.0200.0390.0270.0510.0270.0710.0360.0650.0340.0540.0080.0090.0430.0550.0730.0630.0630.0790.0440.0530.0841.0000.0670.1510.0420.0730.0940.073
keep_alive_session0.0000.0390.0400.0640.0670.0020.0290.0390.0350.0510.0990.0120.0410.0310.0610.0490.1110.0130.0120.0440.0600.0480.0310.0700.0660.0860.0150.0860.0671.0000.0280.1190.0420.0260.047
email_is_free0.0000.0250.0220.0890.0410.0180.0210.0270.0450.0480.0520.0110.0340.0190.0460.0440.0660.0090.0090.0740.0530.0280.0300.0160.0880.0330.0290.0010.1510.0281.0000.0090.0130.0350.027
device_distinct_emails_8w0.0010.0160.0110.0180.0370.0120.0250.0190.0270.0370.0520.0120.0380.0230.0280.0500.0520.0040.0050.0200.0380.0400.0450.0430.0250.0340.0070.4210.0420.1190.0091.0000.0140.0690.027
phone_home_valid0.0040.0130.0510.1420.2530.0530.0260.0880.0530.0560.0930.0750.0190.0810.0470.0380.1010.0180.0190.0300.2220.0460.0840.1740.1170.1050.0090.0110.0730.0420.0130.0141.0000.2850.199
phone_mobile_valid0.0020.0370.0230.1110.1740.0150.0630.0360.0310.0410.0700.0240.0340.0310.0300.0170.0750.0020.0030.0430.1420.0080.0690.1770.0980.0170.0090.0240.0940.0260.0350.0690.2851.0000.142
segmentacion_etaria0.0000.0760.0350.1061.0000.0200.0200.0510.0340.0310.0270.0420.0830.0520.0770.0250.0300.0070.0070.0460.2550.0250.0420.2160.2350.1100.0070.0280.0730.0470.0270.0270.1990.1421.000

Missing values

2023-05-17T18:22:37.877024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-17T18:22:39.966388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idfraud_boolincomeprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wemployment_statuscredit_risk_scorehousing_statusbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_fraud_countmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_validsegmentacion_etaria
07295170.00.7-1.0305.060.00.030059-1.599455AC990.05340.1316053945.8712645531.6108891.0CC147.0BB-1.00.0500.00.0INTERNET8.865992windows0.00.02.0-0.2454250.5688110.8834852.01.01.00.01.0Persona Mayor
21495850.00.8-1.0140.050.00.0156593.951994AA1269.02844.2600152789.8273553141.1373621605.0CF105.0BA9.01.0200.00.0INTERNET4.654872linux1.00.07.00.009336-2.0966820.1132084.01.01.01.01.0Adulto
4644860.00.9-1.0171.050.00.00140928.159779AB4430.05854.0657904210.0390955313.23394317.0CC208.0BA1.01.01500.00.0INTERNET3.720953linux1.00.01.02.229616-0.0058230.7927974.00.01.00.01.0Adulto
58252830.00.5-1.085.030.00.027292-1.310498AB1698.07938.5257094963.1946195070.085151872.0CA138.0BE3.00.0200.00.0INTERNET2.912670windows1.00.03.0-0.1939450.8612070.86508210.00.01.00.01.0Adulto
783080.00.9-1.039.030.00.010945-1.450972AC569.05268.9752645573.9329974931.7516481.0CA110.0BB-1.01.0200.00.0INTERNET2.283680other1.00.03.0-2.4406500.3549860.45002413.01.01.00.00.0Adulto
98916360.00.6-1.0137.060.00.04615825.841925AA1512.07669.4941454251.2576825721.121146271.0CC33.0BB28.00.0200.00.0INTERNET20.889553linux0.00.02.02.1929600.4840680.3832382.01.01.01.01.0Persona Mayor
113950340.00.4-1.0156.040.00.03228897.769357AA941.06073.8569533653.0853584280.4979411322.0CA91.0BA28.00.0200.00.0INTERNET8.056782linux0.00.06.0-0.647299-0.3195440.9038355.01.01.01.01.0Adulto
123503240.00.3-1.061.050.00.010861-1.194354AB2618.05697.3329653573.0925034329.13305241.0CB65.0BA25.00.0200.00.0INTERNET8.540440windows1.00.04.00.337705-1.6340930.0282848.00.01.01.01.0Adulto
138527330.00.1-1.0109.030.023.613744-0.988608AB925.05709.4275293101.4075274913.06082912.0CB167.0BB31.00.0200.00.0INTERNET5.278296other1.00.03.01.039810-1.8917280.32984315.01.01.00.01.0Adulto
152145410.00.6-1.0171.020.00.011327-0.709331AB470.02627.2483292989.1804403119.99488013.0CA117.0BE1.00.0200.00.0INTERNET1.304664linux1.00.07.0-0.497989-0.0509750.9029874.00.01.00.01.0Adulto-Joven
idfraud_boolincomeprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wemployment_statuscredit_risk_scorehousing_statusbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_fraud_countmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_validsegmentacion_etaria
1137182120580.00.4-1.019.060.00.040269-1.165111AB1208.08593.1147234745.9579774209.3856935.0CA107.0BB26.01.0200.00.0INTERNET3.724164windows0.00.05.0-1.498585-1.0093960.6318826.00.01.01.00.0Persona Mayor
11371831156710.00.9-1.0194.030.00.030739-0.445877AD2327.01248.4048553606.6744654102.1169830.0CA41.0BE30.00.0200.00.0INTERNET5.903499windows0.00.05.0-1.3662981.0976450.50553011.00.01.00.01.0Adulto
11371846783380.00.4-1.026.030.00.006036-1.369820AC3553.05781.3401847125.1576595674.3273850.0CF135.0BB-1.00.0200.00.0INTERNET26.800081windows0.00.02.01.4890940.0044340.34263711.01.01.00.01.0Adulto
11371858372690.00.8-1.0148.050.00.010308-1.010951AB842.06852.7628844840.8398644437.81246039.0CF138.0BA20.00.0200.00.0INTERNET3.383891windows1.00.05.0-0.3372470.1055990.5352564.01.01.00.01.0Adulto
11371866174360.00.8-1.011.030.00.009451-0.572828AB2816.07044.3207486149.5011666350.1434709.0CA85.0BC1.00.0200.00.0INTERNET4.413203linux1.00.00.00.5563210.8842730.2299769.01.01.00.01.0Adulto
11371877070630.00.1-1.0260.060.00.026023-0.587295AB1795.02647.0653693186.0177234295.3115792.0CA185.0BE20.00.01000.00.0INTERNET5.780150windows0.00.05.00.397897-0.1035410.8701183.01.01.00.00.0Persona Mayor
11371887846060.00.5-1.030.050.00.052916-1.249585AB1039.013107.1815127959.5972435463.1321935.0CE177.0BB25.01.0200.00.0INTERNET3.359270macintosh1.00.01.01.713050-1.9116690.0947155.00.01.01.01.0Adulto
11371892163750.00.846.00.030.00.005857-0.380745AB918.01804.3781873607.5875174955.99397910.0CA215.0BC28.01.01500.00.0INTERNET4.821661windows0.00.03.0-0.727536-1.5409930.43702613.00.01.00.01.0Adulto
1137190418560.00.725.018.030.01.42664410.441915AA1188.04609.8740716271.0510814860.1186219.0CA122.0BC15.00.0200.00.0INTERNET2.899507other1.00.03.01.642429-0.0267830.44901015.00.01.01.01.0Adulto
11371919881910.00.9-1.056.030.00.006165-1.103130AD1922.02608.6150314771.4996954950.83261142.0CA100.0BC5.01.0200.00.0INTERNET3.843607linux0.00.04.0-1.6622471.5762140.76995910.01.01.01.01.0Adulto